Package: PCAmixdata 3.1

PCAmixdata: Multivariate Analysis of Mixed Data

Implements principal component analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables.

Authors:Marie Chavent [aut, cre], Vanessa Kuentz [aut], Amaury Labenne [aut], Benoit Liquet [aut], Jerome Saracco [aut]

# Install 'PCAmixdata' in R:

install.packages('PCAmixdata', repos = c('https://chavent.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

On CRAN:

Last updated 2 years agofrom:428226c55b. Checks:8 OK. Indexed: yes.

Dependencies:

Marie Chavent

Last update: 2019-04-04
Started: 2017-08-28

Marie Chavent

Last update: 2017-08-28
Started: 2017-08-28

Help page	Topics
Performance in decathlon (data)	decathlon
Breeds of Dogs data	dogs
Flower Characteristics	flower
gironde	gironde
Multiple factor analysis of mixed data	MFAmix
Principal component analysis of mixed data	PCAmix
Varimax rotation in PCAmix	PCArot
Graphical outputs of MFAmix	plot.MFAmix
Graphical outputs of PCAmix and PCArot	plot.PCAmix
Prediction of new scores in MFAmix	predict.MFAmix
Prediction of new scores in PCAmix or PCArot	predict.PCAmix
Print a 'MFAmix' object	print.MFAmix
Print a 'PCAmix' object	print.PCAmix
Protein data	protein
Recoding of the data matrices	recod
Recoding of the qualitative data matrix.	recodqual
Recoding of the quantitative data matrix	recodquant
splitgroups	splitgroups
splitmix	splitmix
Summary of a 'MFAmix' object	summary.MFAmix
Summary of a 'PCAmix' object	summary.PCAmix
Supplementary variables projection	supvar
Supplementary variables in MFAmix	supvar.MFAmix
Supplementary variables in PCAmix	supvar.PCAmix
Built an indicator matrix	tab.disjonctif.NA
User satisfaction survey with 1232 individuals and 14 questions	vnf
Wine	wine